ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation

Muhammad Asad, Lucas Fidon, Tom Vercauteren

Keywords: interactive segmentation, online learning, neural networks, COVID-19

TL;DR: We propose an efficient convolutional neural networks (CNNs) that can be learned online while the annotator provides scribble-based interaction.

Abstract: Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes. Annotations mandate expert knowledge and are time-intensive to obtain through fully manual segmentation methods. Additionally, lung lesions have large inter-patient variations, with some pathologies having similar visual appearance as healthy lung tissues. This poses a challenge when applying existing semi-automatic interactive segmentation techniques for data labelling. To address these challenges, we propose an efficient convolutional neural networks (CNNs) that can be learned online while the annotator provides scribble-based interaction. To accelerate learning from only the samples labelled through user-interactions, a patch-based approach is used for training the network. Moreover, we use weighted cross-entropy loss to address the class imbalance that may result from user-interactions. During online inference, the learned network is applied to the whole input volume using a fully convolutional approach. We compare our proposed method with state-of-the-art using synthetic scribbles and show that it outperforms existing methods on the task of annotating lung lesions associated with COVID-19, achieving 16% higher Dice score while reducing execution time by 3$\times$ and requiring 9000 lesser scribbles-based labelled voxels. Due to the online learning aspect, our approach adapts quickly to user input, resulting in high quality segmentation labels. Source code for ECONet is available at: https://github.com/masadcv/ECONet-MONAILabel

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Paper Type: both

Primary Subject Area: Segmentation

Secondary Subject Area: Learning with Noisy Labels and Limited Data

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Code And Data: https://github.com/masadcv/ECONet-MONAILabel

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2201.04584/code)